code: def __init__(self, config):
      super().__init__()
      self.config = config
      self.train_batch_sizes = self.config.train_batch_sizes
      self.val_batch_sizes = self.config.val_batch_sizes
      self.train_datasets = []
      self.val_datasets = []
      self.g = torch.Generator()
      self.g.manual_seed(self.config.seed)
  
imports: import math
  import random
  from pathlib import Path
  import numpy
  import lightning.pytorch
  import torch
  from lightning.pytorch.utilities import rank_zero_only
  from torch.nn.utils.rnn import pad_sequence
  from torch.optim.lr_scheduler import LambdaLR
  from torch.utils.data import DataLoader
  from nougat import NougatConfig, NougatModel
  from nougat.metrics import get_metrics
label_desc: Use image node_id 1 to create a new NougatDataPLModule object from inherited base classes: pl with example: obj = NougatDataPLModule(config)